{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e74d73c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "\n",
    "import os\n",
    "#os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n",
    "\n",
    "import numpy as np\n",
    "from models import *\n",
    "\n",
    "import torch\n",
    "import torch.optim\n",
    "\n",
    "from skimage.metrics import peak_signal_noise_ratio as compare_psnr\n",
    "from skimage.metrics import mean_squared_error as compare_mse\n",
    "from utils.denoising_utils import *\n",
    "\n",
    "from skimage._shared import *\n",
    "from skimage.util import *\n",
    "from skimage.metrics.simple_metrics import _as_floats\n",
    "from skimage.metrics.simple_metrics import mean_squared_error\n",
    "\n",
    "\n",
    "from UtilityMine import *\n",
    "from utils.sr_utils import tv_loss\n",
    "from numpy import linalg as LA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1df1cd91",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.backends.cudnn.enabled = True\n",
    "torch.backends.cudnn.benchmark =True\n",
    "dtype = torch.cuda.FloatTensor\n",
    "\n",
    "PLOT = False\n",
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da506333",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname2  = \"Data/DC1/Y_clean.mat\"\n",
    "mat2 = scipy.io.loadmat(fname2)\n",
    "img_np_gt = mat2[\"Y_clean\"]\n",
    "img_np_gt = img_np_gt.transpose(2,0,1)\n",
    "[p1, nr1, nc1] = img_np_gt.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5c442eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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